# centrality(correct_adj, type = c("indegree")) +
#  centrality(affirm_adj, type = c("indegree")) +
#  covariate(reciprocity_rate_list)+
#   covariate(days_list)
#  covariate(age_list) +
#  covariate(black_list) +
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_3 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")),
#  covariate(reciprocity_rate_list)+
#   covariate(days_list)
#  covariate(age_list) +
#  covariate(black_list) +
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_4 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate"),
#   covariate(days_list)
#  covariate(age_list) +
#  covariate(black_list) +
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_5 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate")+
covariate(days_list, coefname = "Days"),
#  covariate(age_list) +
#  covariate(black_list) +
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_6 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate")+
covariate(days_list, coefname = "Days") +
covariate(age_list, coefname = "Age"),
#  covariate(black_list) +
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_7 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate")+
covariate(days_list, coefname = "Days") +
covariate(age_list, coefname = "Age") +
covariate(black_list, coefname = "Black"),
#  covariate(lsiExit_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_8 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate")+
covariate(days_list, coefname = "Days") +
covariate(age_list, coefname = "Age") +
covariate(black_list, coefname = "Black") +
covariate(lsi_list, coefname = "LSI Entry"),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_9 <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
# netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list, coefname = "Reciprocity Rate")+
covariate(days_list, coefname = "Days") +
covariate(age_list, coefname = "Age") +
covariate(black_list, coefname = "Black") +
covariate(lsi_list, coefname = "LSI Entry"),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
Affirmations_mods <- list(Affirmations_1, Affirmations_2, Affirmations_3, Affirmations_4, Affirmations_5, Affirmations_6, Affirmations_7, Affirmations_8, Affirmations_9)
sink("Table2.txt")
texreg::screenreg(Affirmations_mods,
custom.coef.names = c("Intercept",
"Direct Affirmations, Successful Peers",
"Indirect Affirmations, Successful Peers",
"Affirmations Recieved",
"Rate of Affirmations Recieved Reciprocated",
"Days",
"Age",
"Black",
"LSI Entry",
"Weigted Affirmations, Successful Peers"))
sink()
#################
# Interpretation
#################
### Interpretation: Affirmations_8
Affirmations_8noCoef <- tnam(success_list ~
#netlag(success_list, correct_adj, pathdist = 1) +
netlag(success_list, affirm_adj, pathdist = 1) +
#netlag(success_list, correct_adj, pathdist = 2) +
netlag(success_list, affirm_adj, pathdist = 2) +
#weightlag(success_list, correct_adj) +
#   weightlag(success_list, affirm_adj) +
# centrality(correct_adj, type = c("indegree")) +
centrality(affirm_adj, type = c("indegree")) +
covariate(reciprocity_rate_list)+
covariate(days_list) +
covariate(age_list) +
covariate(black_list) +
covariate(lsi_list),
re.node = FALSE,
time.linear = FALSE,
family = binomial(link="logit"))
data <- Affirmations_8noCoef$model
newdata.1 <- data.frame(
netlag.pathdist1 = data$netlag.pathdist1,
netlag.pathdist2.decay0.5 = mean(data$netlag.pathdist2.decay0.5, na.rm = TRUE),
indegree = mean(data$indegree, na.rm = TRUE),
covariate.x = mean(data$covariate.x),
covariate.y = mean(data$covariate.y),
covariate.x.1 = mean(data$covariate.x.1),
covariate.y.1 = median(data$covariate.y.1),
covariate = mean(data$covariate)
)
predicted.data.1 <- as.data.frame(predict(Affirmations_8noCoef, newdata = newdata.1,
type="link", se=TRUE))
newdata.1 <- cbind(newdata.1, predicted.data.1)
std <- qnorm(0.95 / 2 + 0.5)
newdata.1$ymin <- Affirmations_8$family$linkinv(newdata.1$fit - std * newdata.1$se)
newdata.1$ymax <- Affirmations_8$family$linkinv(newdata.1$fit + std * newdata.1$se)
newdata.1$fit <- Affirmations_8$family$linkinv(newdata.1$fit)
library(ggplot2)
mod1  <- ggplot(data, aes(x=netlag.pathdist1)) +
geom_ribbon(data = newdata.1, aes(y=fit, ymin=ymin, ymax=ymax), alpha = 0.5) +
geom_line(data = newdata.1, aes(x = netlag.pathdist1, y=fit), size = 1.5, colour = "firebrick4") +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))+
annotate("label", x = 40, y = 0.7, label = 'bold("A: Direct, Successful Peers")', parse = TRUE, size = 6) +
# ggtitle("A: Direct Affirmations, Successful Peers") +
labs(x="Successful Alters [Direct Effect]", y="Probability of Graduation")
mod1
interp.data.frame.1 <- newdata.1[1:5,]
interp.data.frame.1$first.order <- NA
interp.data.frame.1[1,]$first.order <- with(newdata.1, mean(netlag.pathdist1) - 2*sd(netlag.pathdist1))
interp.data.frame.1[2,]$first.order <- with(newdata.1,  mean(netlag.pathdist1) - 1*sd(netlag.pathdist1))
interp.data.frame.1[3,]$first.order <- with(newdata.1,  mean(netlag.pathdist1))
interp.data.frame.1[4,]$first.order <- with(newdata.1,  mean(netlag.pathdist1) + 1*sd(netlag.pathdist1))
interp.data.frame.1[5,]$first.order <- with(newdata.1,  mean(netlag.pathdist1) + 2*sd(netlag.pathdist1))
interp.data.frame.1$second.order <- NA
interp.data.frame.1$second.order <-with(newdata.1, mean(netlag.pathdist2.decay0.5))
data$first.order <- data$netlag.pathdist1
first.order.model <- glm(response ~
first.order +
netlag.pathdist2.decay0.5 +
indegree +
covariate.x + covariate.y + covariate.x.1 + covariate.y.1 + covariate,
data = data,
family = binomial)
predicted.data <- as.data.frame(predict(first.order.model, newdata = interp.data.frame.1,
type="link", se=TRUE))
new.data <- cbind(predicted.data)
std <- qnorm(0.95 / 2 + 0.5)
new.data$ymin <- Affirmations_8noCoef$family$linkinv(new.data$fit - std * new.data$se.fit)
new.data$ymax <- Affirmations_8noCoef$family$linkinv(new.data$fit + std * new.data$se.fit)
new.data$fit <- Affirmations_8noCoef$family$linkinv(new.data$fit)
newdata.2 <- data.frame(
netlag.pathdist1 = mean(data$netlag.pathdist1),
netlag.pathdist2.decay0.5 = data$netlag.pathdist2.decay0.5,
indegree = mean(data$indegree, na.rm = TRUE),
covariate.x = mean(data$covariate.x),
covariate.y = mean(data$covariate.y),
covariate.x.1 = mean(data$covariate.x.1),
covariate.y.1 = median(data$covariate.y.1),
covariate = mean(data$covariate)
)
predicted.data.2 <- as.data.frame(predict(Affirmations_8noCoef, newdata = newdata.2,
type="link", se=TRUE))
newdata.2 <- cbind(newdata.2, predicted.data.2)
newdata.2$ymin <- Affirmations_8$family$linkinv(newdata.2$fit - std * newdata.2$se)
newdata.2$ymax <- Affirmations_8$family$linkinv(newdata.2$fit + std * newdata.2$se)
newdata.2$fit <- Affirmations_8$family$linkinv(newdata.2$fit)
mod2  <- ggplot(data, aes(x=netlag.pathdist2.decay0.5)) +
geom_ribbon(data = newdata.2, aes(y=fit, ymin=ymin, ymax=ymax), alpha = 0.5) +
geom_line(data = newdata.2, aes(x = netlag.pathdist2.decay0.5, y=fit), size = 1.5, colour = "firebrick4") +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))+
annotate("label", x = 62.5, y = 0.7, label = 'bold("B: Indirect, Successful Peers")', parse = TRUE, size = 6) +
#ggtitle("B: Indirect Affirmations, Successful Peers") +
labs(x="Successful Alters [Indirect Effect]", y=NULL)
mod2
interp.data.frame.2 <- newdata.2[1:5,]
interp.data.frame.2$second.order <- NA
interp.data.frame.2[1,]$second.order <- with(newdata.2, mean(netlag.pathdist2.decay0.5) - 2*sd(netlag.pathdist2.decay0.5))
interp.data.frame.2[2,]$second.order <- with(newdata.2,  mean(netlag.pathdist2.decay0.5) - 1*sd(netlag.pathdist2.decay0.5))
interp.data.frame.2[3,]$second.order <- with(newdata.2,  mean(netlag.pathdist2.decay0.5))
interp.data.frame.2[4,]$second.order <- with(newdata.2,  mean(netlag.pathdist2.decay0.5) + 1*sd(netlag.pathdist2.decay0.5))
interp.data.frame.2[5,]$second.order <- with(newdata.2,  mean(netlag.pathdist2.decay0.5) + 2*sd(netlag.pathdist2.decay0.5))
interp.data.frame.2$first.order <- NA
interp.data.frame.2$first.order <-with(newdata.2, mean(netlag.pathdist1))
data$second.order <- data$netlag.pathdist2.decay0.5
second.order.model <- glm(response ~
second.order +
netlag.pathdist1 +
indegree +
covariate.x + covariate.y + covariate.x.1 + covariate.y.1 + covariate,
data = data,
family = binomial)
predicted.data <- as.data.frame(predict(second.order.model, newdata = interp.data.frame.2,
type="link", se=TRUE))
new.data.2 <- cbind(predicted.data)
std <- qnorm(0.95 / 2 + 0.5)
new.data.2$ymin <- Affirmations_8noCoef$family$linkinv(new.data.2$fit - std * new.data.2$se.fit)
new.data.2$ymax <- Affirmations_8noCoef$family$linkinv(new.data.2$fit + std * new.data.2$se.fit)
new.data.2$fit <- Affirmations_8noCoef$family$linkinv(new.data.2$fit)
source("multiplot.R")
png("Figure2.png", width = 900)
multiplot(mod1, mod2, cols = 2)
dev.off()
loocv_predictions_baseline <- list()
loocv_predictions_net1 <- list()
loocv_predictions_net2 <- list()
loocv_predictions_both <- list()
data <- Affirmations_8$model
data <- with(data,
data.frame(
success = response,
net1 = netlag.pathdist1,
net2 = netlag.pathdist2.decay0.5,
indegree = indegree,
reciprocity = `covariate.Reciprocity Rate`,
days = covariate.Days,
age = covariate.Age,
black = covariate.Black,
lsi = `covariate.LSI Entry`
))
colnames(data)
baseline_formula <- as.formula(success ~ indegree + reciprocity + days + age + black + lsi)
net1_formula <- as.formula(success ~ net1 + indegree + reciprocity + days + age + black + lsi)
net2_formula <- as.formula(success ~ net2 + indegree + reciprocity + days + age + black + lsi)
both_formula <- as.formula(success ~ net1 + net2 + indegree + reciprocity + days + age + black + lsi)
for(i in 1:nrow(data)){
test_data <- data[i,]
train_data <- data[-i,]
# Base Model
baseMod <- glm(baseline_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = train_data,
control = glm.control(epsilon = 0.0001, maxit = 100))
predicted.data.base <- as.data.frame(predict(baseMod, newdata = test_data,
type="response", se=TRUE))
loocv_predictions_baseline[[i]] <- predicted.data.base$fit
# Net 1 Model
net1Mod <- glm(net1_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = train_data,
control = glm.control(epsilon = 0.0001, maxit = 100))
predicted.data.net1 <- as.data.frame(predict(net1Mod, newdata = test_data,
type="response", se=TRUE))
loocv_predictions_net1[[i]] <- predicted.data.net1$fit
# Net 2 Model
net2Mod <- glm(net2_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = train_data,
control = glm.control(epsilon = 0.0001, maxit = 100))
predicted.data.net2 <- as.data.frame(predict(net2Mod, newdata = test_data,
type="response", se=TRUE))
loocv_predictions_net2[[i]] <- predicted.data.net2$fit
# Both Model
bothMod <- glm(both_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = train_data,
control = glm.control(epsilon = 0.0001, maxit = 100))
predicted.data.both <- as.data.frame(predict(bothMod, newdata = test_data,
type="response", se=TRUE))
loocv_predictions_both[[i]] <- predicted.data.both$fit
indices <- c(250, 500, 750, 1000, 1250)
if(i %in% indices){
print(paste0(i, "iterations complete"))
}
}
baseMod <- glm(baseline_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = data,
control = glm.control(epsilon = 0.0001, maxit = 100))
data$baseMod_fitted <- as.data.frame(predict(baseMod, newdata = data,
type="response", se=TRUE))
net1Mod <- glm(net1_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = data,
control = glm.control(epsilon = 0.0001, maxit = 100))
data$net1_fitted <- as.data.frame(predict(net1Mod, newdata = data,
type="response", se=TRUE))
net2Mod <- glm(net2_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = data,
control = glm.control(epsilon = 0.0001, maxit = 100))
data$net2_fitted <- as.data.frame(predict(net2Mod, newdata = data,
type="response", se=TRUE))
bothMod <- glm(both_formula,
model = TRUE,
family = binomial(link = "logit"),
na.action = na.omit,
data = data,
control = glm.control(epsilon = 0.0001, maxit = 100))
data$both_fitted <- as.data.frame(predict(bothMod, newdata = data,
type="response", se=TRUE))
data$loocv_predictions_baseline <- unlist(loocv_predictions_baseline)
data$loocv_predictions_both <- unlist(loocv_predictions_both)
data$loocv_predictions_net1 <- unlist(loocv_predictions_net1)
data$loocv_predictions_net2 <- unlist(loocv_predictions_net2)
# Comparing PR Curves
library(DMwR)
# v. 0.4.1
library(ROCR)
# v. 1.0-7
library(caTools)
# v. 1.17.1
auc_pr <- function(obs, pred) {
xx.df <- prediction(pred, obs)
perf  <- performance(xx.df, "prec", "rec")
xy    <- data.frame(recall=perf@x.values[[1]], precision=perf@y.values[[1]])
# take out division by 0 for lowest threshold
xy <- subset(xy, !is.nan(xy$precision))
res   <- trapz(xy$recall, xy$precision)
res
}
# get plot coordinates
rocdf <- function(pred, obs, data=NULL, type=NULL) {
# plot_type is "roc" or "pr"
if (!is.null(data)) {
pred <- eval(substitute(pred), envir=data)
obs  <- eval(substitute(obs), envir=data)
}
rocr_xy <- switch(type, roc=c("tpr", "fpr"), pr=c("prec", "rec"))
rocr_df <- prediction(pred, obs)
rocr_pr <- performance(rocr_df, rocr_xy[1], rocr_xy[2])
xy <- data.frame(rocr_pr@x.values[[1]], rocr_pr@y.values[[1]])
colnames(xy) <- switch(type, roc=c("tpr", "fpr"), pr=c("rec", "prec"))
return(xy)
}
# Plots -- full model
# Calculate AUC
bothAUC <- auc_pr(data$success, data$loocv_predictions_both)
# 0.9312698
xyBoth <- rocdf(data$loocv_predictions_both,
data$success,
type="pr")
library(ggplot2)
# v 1.0.1
plotBoth <- ggplot(xyBoth, aes(xyBoth$rec, xyBoth$prec)) +
geom_line(aes(xyBoth$rec, xyBoth$prec), linetype = 1, size = 2, colour = "firebrick4") +
xlab("Recall") +
ylab("Precision") +
annotate("label", x = 0.5, y = 0.683, label = "AUC = 0.9312698") +
annotate("label", x = 0.5, y = 0.7, label = 'bold("D: Full Model")', parse = TRUE, size = 6) +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# Plots -- non-network model
# Calculate AUC
baseAUC <- auc_pr(data$success, data$loocv_predictions_baseline)
# 0.9103609
xyBase <- rocdf(data$loocv_predictions_baseline,
data$success,
type="pr")
plotBase <- ggplot(xyBase, aes(xyBase$rec, xyBase$prec)) +
geom_line(aes(xyBase$rec, xyBase$prec), linetype = 1, size = 2, colour = "firebrick4") +
xlab("Recall") +
ylab("Precision") +
annotate("label", x = 0.5, y = 0.683, label = "AUC = 0.9103609") +
annotate("label", x = 0.5, y = 0.7, label = 'bold("A: Baseline Model")', parse = TRUE, size = 6) +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# Plots -- first-term model
# Calculate AUC
net1AUC <- auc_pr(data$success, data$loocv_predictions_net1)
# 0.9247763
xyNet1 <- rocdf(data$loocv_predictions_net1,
data$success,
type="pr")
plotNet1 <- ggplot(xyNet1, aes(xyNet1$rec, xyNet1$prec)) +
geom_line(aes(xyNet1$rec, xyNet1$prec), linetype = 1, size = 2, colour = "firebrick4") +
xlab("Recall") +
ylab("Precision") +
annotate("label", x = 0.5, y = 0.683, label = "AUC = 0.9247763") +
annotate("label", x = 0.5, y = 0.7, label = 'bold("B: Direct Effect Model")', parse = TRUE, size = 6) +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
# Plots -- second-term model
# Calculate AUC
net2AUC <- auc_pr(data$success, data$loocv_predictions_net2)
# 0.9294625
xyNet2 <- rocdf(data$loocv_predictions_net2,
data$success,
type="pr")
plotNet2 <- ggplot(xyNet2, aes(xyNet2$rec, xyNet2$prec)) +
geom_line(aes(xyNet2$rec, xyNet2$prec), linetype = 1, size = 2, colour = "firebrick4") +
xlab("Recall") +
ylab("Precision") +
annotate("label", x = 0.5, y = 0.683, label = "AUC = 0.9294625") +
annotate("label", x = 0.5, y = 0.7, label = 'bold("C: Indirect Effect Model")', parse = TRUE, size = 6) +
scale_y_continuous(limits=c(0.65,1)) +
theme(legend.position = c(0.2, 0.8),
axis.text=element_text(size=12),
axis.title=element_text(size=14,face="bold"))
plotNet2
source("multiplot.R")
png("Figure3.png", width = 850, height = 850)
multiplot(plotBase, plotNet2, plotNet1, plotBoth, cols = 2)
dev.off()
#######################
# Description variables
#######################
source("describe.R")
dat <- Affirmations_8$model
dat[dat$covariate.Days < 0,]$covariate.Days <- NA
outcome <- describe(dat$response, type = "binary", plot_title = "TC Graduation")
net1 <- describe(dat$netlag.pathdist1, type = "continuous", plot_title = "Direct Affirmations")
net2 <- describe(dat$netlag.pathdist2.decay0.5, type = "continuous", plot_title = "Indirect Affirmations")
indegree <- describe(dat$indegree, type = "continuous", plot_title = "In-Degree Centrality")
reciprocity <- describe(dat$`covariate.Reciprocity Rate`, type = "continuous", plot_title = "Reciprocity Rate")
days <- describe(dat$covariate.Days, type = "continuous", plot_title = "Days")
age <- describe(dat$covariate.Age, type = "continuous", plot_title = "Age")
black <- describe(dat$covariate.Black, type = "binary", plot_title = "Race")
lsi <- describe(dat$`covariate.LSI Entry`, type = "continuous", plot_title = "Entry LSI")
# rm(list=setdiff(ls(), "dat"))
dat <- Affirmations_9$model
dat[dat$covariate.Days < 0,]$covariate.Days <- NA
weight <- describe(dat$weightlag, type = "continuous", plot_title = "Weighted Affirmations")
mean(dat$weightlag)
sd(dat$weightlag)
dist <- list(outcome = outcome$dist, direct = net1$dist, indirect = net2$dist,
indegree = indegree$dist, reciprocity = reciprocity$dist, days= days$dist,
age = age$dist, black = black$dist, lsi = lsi$dist, weight = weight$dist)
sink("Table1.txt")
dist
sink()
?source
